AI News, Rules vs Scores: keeping fraud simple with Machine Learning

Rules vs Scores: keeping fraud simple with Machine Learning

Being too busy with protecting their business rather than offering unmatched products and customer experiences, merchants often get lost while searching for the best way to successfully manage fraud.

Machine learning systems are self-learning models, built using advanced mathematical algorithms to detect patterns and make predictions based on vast streams of normalized data.

A fraud prevention ML model evaluates various signals and data sets, calculating the legitimacy of the transactions to precisely detect the fraudulent ones.

Furthermore, while traditional solutions can only calculate the outcome based on pre-programmed rules, ML is much more flexible, correlating various elements and working with past and new data features.

Machine learning technology also detects patterns that are not often visible to fraud analysts, thus lifting their workload and raising the flag so that preventative action can be taken before it’s too late.

Empowered fraud managers In addition to the improved speed and efficiency, machine learning also empowers fraud professionals to perform multiple reviews and analyses at the same time.

Simultaneously, a machine learning model can also learn behavior patterns, integrating the outcomes and feedback provided by the analysts and using this data to continuously improve its predictive capabilities.

Next to that, by using machine learning, fraud managers have more time to improve operational efficiency and focus on driving conversion to add more value to the business.

Happy customers = increased conversion Inflexible rule-based fraud solutions deliver too many false positives, locking out genuine buyers or causing obstacles in their shopping journey.

At the same time, machine learning removes a lot of the hurdles and friction for legitimate consumers, ensuring that they enjoy a pleasant shopping journey and that they’re able to pay quickly and seamlessly.

The most important elements include shopping behavior, transaction amount, BIN, currency, IP and billing address, device type, payment method, email address, order history and much more.

Basically, with the help of machine learning technology, Acapture can instantly predict whether a customer is trustworthy or not so that they can approve the order, block it or perform additional screening, all while keeping genuine customers happy.

He has been at the forefront of fraud innovation for his entire career, working with corporates, start-ups and SMEs from every corner of business culture to bring products and commercial strategies to new levels of accuracy and effectiveness.

Being too busy with protecting their business rather than offering unmatched products and customer experiences, merchants often get lost while searching for the best way to successfully manage fraud.

Machine learning systems are self-learning models, built using advanced mathematical algorithms to detect patterns and make predictions based on vast streams of normalized data.

Furthermore, while traditional solutions can only calculate the outcome based on pre-programmed rules, ML is much more flexible, correlating various elements and working with past and new data features.

Machine learning technology also detects patterns that are not often visible to fraud analysts, thus lifting their workload and raising the flag so that preventative action can be taken before it’s too late.

Empowered fraud managers In addition to the improved speed and efficiency, machine learning also empowers fraud professionals to perform multiple reviews and analyses at the same time.

Simultaneously, a machine learning model can also learn behavior patterns, integrating the outcomes and feedback provided by the analysts and using this data to continuously improve its predictive capabilities.

Happy customers = increased conversion Inflexible rule-based fraud solutions deliver too many false positives, locking out genuine buyers or causing obstacles in their shopping journey.

At the same time, machine learning removes a lot of the hurdles and friction for legitimate consumers, ensuring that they enjoy a pleasant shopping journey and that they’re able to pay quickly and seamlessly.

The most important elements include shopping behavior, transaction amount, BIN, currency, IP and billing address, device type, payment method, email address, order history and much more.

Basically, with the help of machine learning technology, Acapture can instantly predict whether a customer is trustworthy or not so that they can approve the order, block it or perform additional screening, all while keeping genuine customers happy.

He has been at the forefront of fraud innovation for his entire career, working with corporates, start-ups and SMEs from every corner of business culture to bring products and commercial strategies to new levels of accuracy and effectiveness.

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